Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "110" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 31 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 31 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460014 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.724657 | 13.981809 | 4.688751 | -0.145787 | 3.497838 | 1.178699 | -0.042793 | -0.346190 | 0.5359 | 0.5330 | 0.3385 | nan | nan |
| 2460013 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 15.764857 | 11.464599 | 6.491632 | -0.035086 | 6.123384 | -0.392613 | 0.764395 | -0.473238 | 0.5022 | 0.5646 | 0.3286 | nan | nan |
| 2460012 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 17.222396 | 10.872372 | 6.354198 | -0.186416 | 6.840344 | -0.168008 | 3.929095 | -0.656151 | 0.4964 | 0.5663 | 0.3202 | nan | nan |
| 2460011 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 19.533062 | 11.217625 | 8.314730 | -0.393356 | 10.753979 | -0.423341 | 1.906762 | -0.308923 | 0.5031 | 0.5754 | 0.3167 | nan | nan |
| 2460010 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 28.711605 | 4.820384 | 7.172651 | -0.124669 | 7.428669 | -0.313801 | 1.223560 | -0.353054 | 0.4784 | 0.6140 | 0.3381 | nan | nan |
| 2460009 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 29.468134 | 3.974465 | 7.863150 | -0.035822 | 4.469703 | 0.011529 | 3.045320 | -0.266597 | 0.4772 | 0.6132 | 0.3391 | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 13.464846 | 5.424194 | 8.026122 | -0.108038 | 7.167855 | 0.182751 | 4.593125 | -0.328543 | 0.6067 | 0.6650 | 0.3043 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 13.925970 | 11.513900 | 6.713123 | 0.150262 | 7.134377 | -0.040513 | 2.745780 | -0.282844 | 0.5395 | 0.5960 | 0.3119 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 98.41% | 98.91% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.3829 | 0.3047 | 0.2800 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 15.533189 | 12.200496 | 6.299688 | 0.241611 | 6.399338 | 0.220846 | 1.487795 | -0.329127 | 0.5073 | 0.5920 | 0.3404 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 23.348219 | 13.503149 | 6.820589 | 0.028381 | 4.027373 | -0.083331 | 1.523231 | -0.511062 | 0.4888 | 0.6066 | 0.3367 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 26.856834 | 13.874953 | 8.919403 | 0.236293 | 1.758064 | -0.107350 | 1.059401 | 0.228184 | 0.4721 | 0.6015 | 0.3428 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 16.144025 | 14.241494 | 7.805862 | -0.025267 | 5.367926 | -0.188734 | 0.283571 | -0.531686 | 0.5382 | 0.6058 | 0.3413 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.730623 | 14.178445 | 6.715836 | 0.154181 | 6.694004 | -0.194629 | 0.087302 | -0.744506 | 0.5598 | 0.6012 | 0.3415 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.625356 | 16.895049 | 5.924983 | -0.063684 | 2.479616 | 0.091646 | 0.118785 | -0.721488 | 0.5867 | 0.6006 | 0.3594 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.807796 | 17.181958 | 6.828126 | 0.104139 | 2.533375 | -0.210240 | -0.188499 | -0.283594 | 0.5874 | 0.5929 | 0.3620 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.49% | 0.00% | 99.51% | - | - | 14.282369 | 0.917059 | 10.420602 | -0.563787 | 3.711602 | 15.198876 | 1.433793 | 0.718330 | 0.1596 | 0.2584 | -0.2244 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 4.27% | 0.00% | 95.68% | - | - | 14.887953 | 2.494910 | 9.404551 | 4.556039 | 3.662747 | 4.010068 | 0.988458 | -0.154005 | 0.1590 | 0.2602 | -0.2129 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 0.00% | 100.00% | - | - | 18.221596 | 0.361983 | 11.027667 | 4.806374 | 5.106588 | 0.914730 | 1.077557 | -0.152938 | 0.1703 | 0.2565 | -0.2218 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 11.35% | 0.00% | 83.90% | - | - | 14.920585 | 9.172135 | 10.588559 | 5.195435 | 2.610526 | 8.213184 | 2.693735 | 1.050374 | 0.1738 | 0.2818 | -0.1858 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 3.56% | 0.00% | 91.74% | - | - | 17.898537 | 6.962428 | 11.521450 | 5.271111 | 4.318606 | 9.089090 | 3.733232 | 2.710566 | 0.2425 | 0.3590 | -0.1729 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 33.35% | 0.00% | 63.14% | - | - | 17.820367 | 18.613710 | 10.866348 | 5.454503 | 2.925710 | 8.083576 | 2.412832 | 3.841618 | 0.1630 | 0.2857 | -0.1675 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 54.14% | 0.00% | 38.94% | - | - | 17.385786 | 28.892854 | 11.315183 | 6.289829 | 4.097454 | 2.767764 | 2.072405 | 0.408682 | 0.1996 | 0.3377 | -0.1229 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 80.17% | 41.00% | 5.46% | - | - | 19.990522 | 24.317950 | 10.771407 | 4.570486 | 5.383303 | 3.766444 | 3.107744 | 2.850178 | 0.1638 | 0.2611 | -0.0476 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 20.217908 | 20.336428 | 11.187717 | 11.548370 | 4.559078 | 5.293045 | 2.411807 | 3.211984 | 0.0234 | 0.0248 | 0.0009 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 24.058228 | 24.438393 | 14.069561 | 14.374434 | 10.351952 | 12.072186 | 3.102879 | 3.290865 | 0.0234 | 0.0255 | 0.0013 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 23.643355 | 24.057818 | 12.603474 | 13.060695 | 8.968697 | 10.562797 | 5.395056 | 5.486669 | 0.0234 | 0.0251 | 0.0010 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 24.487587 | 24.764298 | 11.811516 | 12.306909 | 8.868857 | 9.875810 | 2.652706 | 2.808945 | 0.0251 | 0.0247 | 0.0010 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 24.878177 | 25.241002 | 12.801005 | 13.251850 | 9.282270 | 10.744383 | 3.241307 | 3.490288 | 0.0225 | 0.0233 | 0.0007 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.281978 | 26.107541 | 12.451573 | 12.937612 | 9.177706 | 10.998471 | 3.284585 | 3.534171 | 0.0236 | 0.0253 | 0.0012 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 25.100941 | 25.487405 | 13.171044 | 13.566690 | 9.353385 | 10.631080 | 2.890400 | 2.874353 | 0.0243 | 0.0264 | 0.0012 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 13.981809 | 0.724657 | 13.981809 | 4.688751 | -0.145787 | 3.497838 | 1.178699 | -0.042793 | -0.346190 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 15.764857 | 15.764857 | 11.464599 | 6.491632 | -0.035086 | 6.123384 | -0.392613 | 0.764395 | -0.473238 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 17.222396 | 17.222396 | 10.872372 | 6.354198 | -0.186416 | 6.840344 | -0.168008 | 3.929095 | -0.656151 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 19.533062 | 19.533062 | 11.217625 | 8.314730 | -0.393356 | 10.753979 | -0.423341 | 1.906762 | -0.308923 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 28.711605 | 28.711605 | 4.820384 | 7.172651 | -0.124669 | 7.428669 | -0.313801 | 1.223560 | -0.353054 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 29.468134 | 29.468134 | 3.974465 | 7.863150 | -0.035822 | 4.469703 | 0.011529 | 3.045320 | -0.266597 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 13.464846 | 5.424194 | 13.464846 | -0.108038 | 8.026122 | 0.182751 | 7.167855 | -0.328543 | 4.593125 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 13.925970 | 13.925970 | 11.513900 | 6.713123 | 0.150262 | 7.134377 | -0.040513 | 2.745780 | -0.282844 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 15.533189 | 15.533189 | 12.200496 | 6.299688 | 0.241611 | 6.399338 | 0.220846 | 1.487795 | -0.329127 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 23.348219 | 23.348219 | 13.503149 | 6.820589 | 0.028381 | 4.027373 | -0.083331 | 1.523231 | -0.511062 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 26.856834 | 26.856834 | 13.874953 | 8.919403 | 0.236293 | 1.758064 | -0.107350 | 1.059401 | 0.228184 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 16.144025 | 16.144025 | 14.241494 | 7.805862 | -0.025267 | 5.367926 | -0.188734 | 0.283571 | -0.531686 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 14.178445 | 7.730623 | 14.178445 | 6.715836 | 0.154181 | 6.694004 | -0.194629 | 0.087302 | -0.744506 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 16.895049 | 0.625356 | 16.895049 | 5.924983 | -0.063684 | 2.479616 | 0.091646 | 0.118785 | -0.721488 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 17.181958 | 0.807796 | 17.181958 | 6.828126 | 0.104139 | 2.533375 | -0.210240 | -0.188499 | -0.283594 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Temporal Variability | 15.198876 | 0.917059 | 14.282369 | -0.563787 | 10.420602 | 15.198876 | 3.711602 | 0.718330 | 1.433793 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 14.887953 | 2.494910 | 14.887953 | 4.556039 | 9.404551 | 4.010068 | 3.662747 | -0.154005 | 0.988458 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 18.221596 | 0.361983 | 18.221596 | 4.806374 | 11.027667 | 0.914730 | 5.106588 | -0.152938 | 1.077557 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 14.920585 | 14.920585 | 9.172135 | 10.588559 | 5.195435 | 2.610526 | 8.213184 | 2.693735 | 1.050374 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | ee Shape | 17.898537 | 6.962428 | 17.898537 | 5.271111 | 11.521450 | 9.089090 | 4.318606 | 2.710566 | 3.733232 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 18.613710 | 18.613710 | 17.820367 | 5.454503 | 10.866348 | 8.083576 | 2.925710 | 3.841618 | 2.412832 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 28.892854 | 17.385786 | 28.892854 | 11.315183 | 6.289829 | 4.097454 | 2.767764 | 2.072405 | 0.408682 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 24.317950 | 19.990522 | 24.317950 | 10.771407 | 4.570486 | 5.383303 | 3.766444 | 3.107744 | 2.850178 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 20.336428 | 20.217908 | 20.336428 | 11.187717 | 11.548370 | 4.559078 | 5.293045 | 2.411807 | 3.211984 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 24.438393 | 24.438393 | 24.058228 | 14.374434 | 14.069561 | 12.072186 | 10.351952 | 3.290865 | 3.102879 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 24.057818 | 24.057818 | 23.643355 | 13.060695 | 12.603474 | 10.562797 | 8.968697 | 5.486669 | 5.395056 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 24.764298 | 24.487587 | 24.764298 | 11.811516 | 12.306909 | 8.868857 | 9.875810 | 2.652706 | 2.808945 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 25.241002 | 25.241002 | 24.878177 | 13.251850 | 12.801005 | 10.744383 | 9.282270 | 3.490288 | 3.241307 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 26.107541 | 25.281978 | 26.107541 | 12.451573 | 12.937612 | 9.177706 | 10.998471 | 3.284585 | 3.534171 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 110 | N10 | RF_maintenance | nn Shape | 25.487405 | 25.487405 | 25.100941 | 13.566690 | 13.171044 | 10.631080 | 9.353385 | 2.874353 | 2.890400 |